Ultra-high throughput sequencing approaches, which has high sensitivity and a large dynamic range, has replaced standard microarray platforms for whole transcriptome analysis (Marioni et al. 2008; Asmann et al. 2009; Wang et al. 2009; Marguerat and Bahler 2010). Massive parallel sequencing of millions of transcripts allows digital estimation of gene abundance as opposed to only expression profiles obtained from microarrays, which are dependent upon the hybridization efficiency of probes to the transcripts. The fast evolution of sequencing technologies resulting in an increase of sequencing depth and the decline in cost per base sequenced has further reinforced their position as preferred platforms for mRNA expression analysis (Metzker 2010; Ozsolak and Milos 2011).
The most widely used RNA-seq protocol (Mortazavi et al. 2008) relies upon fragmentation of mRNA generating a library of uniformly distributed fragments of mRNA. This protocol requires large amounts of starting material (10-100 ng of mRNA) limiting its application in many fields such as in developmental biology, where it is impractical to get such large amounts. Furthermore, this protocol maintains the relative order of transcript expression resulting in poor representation of low abundance transcripts at current sequencing depths (Bloom et al. 2009; Fang and Cui 2011). Multireads and biases introduced by transcript length (Oshlack and Wakefield 2009) and random hexamer primer hybridization (Hansen et al. 2010) further restrict reliable quantitation of low abundance transcripts for large mammalian transcriptomes.
To address these limitations, a number of protocols have been developed. While “random priming” strategies (Li et al. 2008; Armour et al. 2009; Adli et al. 2010) amplify starting material (mRNA or cDNA) by exploiting hybridization and extension potential of hexamer/heptamer primers, they often result in low yield of good quality reads arising out of mis-hybridization of primers and primer dimerization. Also, the random priming methods do not discriminate regions of the transcriptome to amplify, specifically low abundance transcripts. This limitation is also seen in other uniform amplification strategies (Tang et al. 2009; Hoeijmakers et al. 2011). Another approach, involving targeted enrichment (Levin et al. 2009; Li et al. 2009; Zhang et al. 2009) requires longer sample preparation steps, larger amounts of RNA and high costs.